Multi-objective optimization of process cogeneration systems with economic, environmental, and social tradeoffs

Process cogeneration is an effective strategy for exploiting the positive aspects of combined heat and power in the process industry. Traditionally, decisions for process cogeneration have been based mostly on economic criteria. With the growing interest in sustainability issues, there is need to consider economic, environmental, and social aspects of cogeneration. The objective of this article is to develop an optimization framework for the design of process cogeneration systems with economic, environmental, and social aspects. Process integration is used as the coordinating framework for the optimization formulation. First, heat integration is carried out to identify the heating utility requirements. Then, a multi-header steam system is designed and optimized for inlet steam characteristics and their impact on power, fixed and operating costs, greenhouse gas emissions, and jobs. A genetic algorithm is developed to solve the optimization problem. Multi-objective tradeoffs between the economic, environmental, and social aspects are studied through Pareto tradeoffs. A case study is solved to illustrate the applicability of the proposed procedure.     

Reactive scheduling in a make-to-order flexible job shop with re-entrant process and assembly: a mathematical programming approach

A mixed-integer linear programming model is presented for the scheduling of flexible job shops, a production mode characteristic of make-to-order industries. Re-entrant process (multiple visits to the same machine group) and a final assembly stage are simultaneously considered in the model. The formulation uses a continuous time representation and optimises an objective function that is a weighted sum of order earliness, order tardiness and in-process inventory. An algorithm for predictive-reactive scheduling is derived from the proposed model to deal with the arrival of new orders. This is illustrated with a realistic example based on data from the mould making industry. Different reactive scheduling scenarios, ranging from unchanged schedule to full re-scheduling, are optimally generated for order insertion in a predictive schedule. Since choosing the most suitable scenario requires balancing criteria of scheduling efficiency and stability, measures of schedule changes were computed for each re-scheduling solution. The short computational times obtained are promising regarding future application of this approach in the manufacturing environment studied.     

Evaluating the environmental friendliness, economics and energy efficiency of chemical processes: heat integration

The design and improvement of chemical processes can be very challenging. The earlier energy conservation, process economics and environmental aspects are incorporated into the process development, the easier and less expensive it is to alter the process design. In this work different process design alternatives with increasing levels of energy integration are considered in combination with evaluations of the process economics and potential environmental impacts. The example studied is the hydrodealkylation (HDA) of toluene to produce benzene. This study examines the possible fugitive and open emissions from the HDA process, evaluates the potential environmental impacts and the process economics considering different process design alternatives. Results of this work show that there are tradeoffs in the evaluation of potential environmental impacts. As the level of energy integration increases process fugitive emissions increase while energy generation impacts decrease. Similar tradeoffs occur for economic evaluations, where the capital and operating costs associated with heat integration could be optimised. From the example designs considered here, an intermediate amount of energy integration produces the most economically beneficial and environmentally friendly process.     

Robust multi-objective optimization for energy production scheduling in microgrids

In order to achieve better economic and environmental benefits of microgrids (MGs) under multiple uncertainties in renewable energy resources and loads, a novel energy production scheduling method is proposed based on robust multi-objective optimization with minimax criterion. Firstly, a mixed integer minimax multi-objective formulation is developed to capture uncertainties as well as minimize economic and environmental objectives. Secondly, the primal problem is decomposed into a bi-level optimization problem, which attempts to seek robust scheduling scheme set under the worst-case realization of uncertainties in a multi-objective framework. Finally, a hierarchical meta-heuristic solution strategy, including multi-objective cross entropy algorithm and δ+ indicator, is designed to solve the reconstructed problem. Numerical results demonstrate that the proposed scheduling method can effectively attenuate the disturbance of uncertainties as well as reduce energy costs and emissions, as compared with single-objective robust optimization and multi-objective optimization scheduling approaches. This study could offer useful insights which help decision-makers balance robustness and comprehensive benefits in the operation of MGs.     

The impact of human consideration,schedule types and product mix on scheduling objectives for unpaced mixed-model assembly lines

There is an increasing awareness in scheduling research that human behaviour needs to be considered explicitly in scheduling models. Although most scheduling literature ignores human behaviour, especially sequence-dependent processing times form a good basis for explicit consideration. Hence, a processing time function is derived that considers the effects of learning, forgetting, fatigue and recovery. The necessity for explicit human consideration can be regarded as most urgent for unpaced highly-manual mixed-model assembly lines. Based on real data a simulation study is conducted to determine the effect of explicit human consideration while also taking into account the effects of different idealised schedule types and the product mix. The results strongly indicate that the product mix has a consistently high impact on scheduling objectives, the schedule type affects lower-level objectives like starving and blocking times to a greater extent than higher-level objectives like makespan and flow time, and that for certain objectives the height of the objective values and the relative favourability of schedule types depends on human consideration.     

The effect of greenhouse gas policy on the design and scheduling of biodiesel plants with multiple feedstocks

With the increasing attention to the environmental impact of discharging greenhouses gases, there has been a growing public pressure to reduce the carbon footprint associated with the use of fossil fuels. In this context, one of the key strategies is the substitution of fossil fuels with biofuels such as biodiesel. The design of biodiesel production facilities has traditionally been carried out based on technical and economic criteria. Greenhouse gas (GHG) policies (e.g., carbon tax, subsidy) have the potential to significantly alter the design of these facilities, the selection of the feedstocks, and the scheduling of multiple feedstocks. The objective of this article is to develop a systematic approach to the design and scheduling of biodiesel production processes while accounting for the effect of GHG policies in addition to the technical, economic, and environmental aspects. An optimization formulation is developed to maximize the profit of the process subject to flowsheet synthesis and performance modeling equations. Furthermore, the carbon footprint is accounted for with the help of a life cycle analysis (LCA). The objective function includes a term which reflects the impact of the LCA of a feedstock and its processing to biodiesel. A multiperiod approach is used to discretize the decision-making horizon into time periods. During each period, decisions are made on the type and flowrate of the feedstocks, as well as the associated design and operating variables. A case study is solved with several scenarios of feedstocks and GHG policies.     

A practical approach to scheduling a multistage,multiprocessor flow-shop problem

This paper describes a rubber manufacturing system and the numerous technological, operating and quality constraints governing its scheduling process. The complex system resembles a flow-shop problem where all jobs share the same general processing order on a progression of machines. The manufacturing system is defined in terms of production stages and parallel processing lines. One stage can process two or more jobs simultaneously. Jobs are often split on the last stage so that two or more parallel processing lines process the same job simultaneously. The scheduling for such a multistage, multiprocessor flow shop is dominated by the quality constraints. A pragmatic approach is provided in which a schedule is constructed through extensive iterations of analysis and simulation.     

A discrete time reactive scheduling model for new order insertion in job shop,make-to-order industries

This research presents a new reactive scheduling methodology for job shop, make-to-order industries. An integer linear programming formulation previously developed by the authors to schedule these types of industries is extended to address the problem of inserting new orders in a predetermined schedule, which is important in order-driven industries. A reactive scheduling algorithm is introduced to iteratively update the schedules. Numerical results on realistic examples of job shops of different sizes illustrate the effectiveness of the approach. In each case, different alternatives for inserting a set of new orders in an initial schedule are optimally generated, enabling the user to choose the most convenient one. Solutions are characterised by measures of scheduling efficiency as well as stability measures that assess the impact of rescheduling operations in a previously defined scheduling solution.     

Optimal scheduling of biocide dosing for seawater-cooled power and desalination plants

Thermal desalination systems are typically integrated with power plants to exploit the excess heat resulting from the power-generation units. Using seawater in cooling the power plant and the desalination system is a common practice in many parts of the world where there is a shortage of freshwater. Biofouling is one of the major problems associated with the usage of seawater in cooling systems. Because of the dynamic variation in the power and water demands as well as the changes in the characteristics of seawater and the process, there is a need to develop an optimal policy for scheduling biocide usage and cleaning maintenance of the heat exchangers. The objective of this article is to introduce a systematic procedure for the optimization of scheduling the dosing of biocide and dechlorination chemicals as well as cleaning maintenance for a power production/thermal desalination plant. A multi-period optimization formulation is developed and solved to determine: the optimal levels of dosing and dechlorination chemicals; the timing of maintenance to clean the heat-exchange surfaces; and the dynamic dependence of the biofilm growth on the applied doses, the seawater-biocide chemistry, the process conditions, and seawater characteristics for each time period. The technical, economic, and environmental considerations of the system are accounted for. A case study is solved to elucidate the applicability of the developed optimization approach.     

A multi-objective fuzzy graph approach for modular formulation considering end-of-life issues

Increased awareness of the negative environmental impact caused by electronic waste has driven electronics manufacturers to re-engineer their product design process and include product end-of-life considerations into their design criteria. Design for the Environment (DfE), as a possible solution, lacks an implementation framework. To address this problem, a fuzzy graph based modular product design methodology is developed to implement DfE strategies in product modular formulation considering multiple product life cycle objectives guided by DfE. A fuzzy connected graph approach is used to present the product structure, whereby fuzzy relationship values are determined by applying Analytic Hierarchy Process (AHP) to life cycle environmental objectives along with other functional and production concerns. Based on the fuzzy connected graph, an optimal modular formulation is searched using the graph-based clustering algorithm to identify the best module configuration. An example is provided to illustrate the methodology and the algorithm presented in this paper.     

Fuzzy inference-based multiple criteria FMS scheduling

This paper proposes a fuzzy inference-based scheduling decision for flexible manufacturing systems (FMS) with multiple objectives. The objectives have different and dynamic preference levels. It is inferred that the changes in the production environment may be sensed by environmental variables. The detected changes are input in a fuzzy inference mechanism, which outputs the current preference levels of all objectives. A multiple criteria scheduling decision is then made, using the partitioned combination of the preference levels. An example of application is presented. Simulation results show very good performance for the proposed system.     

An integrated system solution for supply chain optimization in the chemical process industry

This paper considers a complex scheduling problem in the chemical process industry involving batch production. The application described comprises a network of production plants with interdependent production schedules, multi-stage production at multi-purpose facilities, and chain production. The paper addresses three distinct aspects: (i) a scheduling solution obtained from a genetic algorithm based optimizer, (ii) a mechanism for collaborative planning among the involved plants, and (iii) a tool for manual updates and schedule changes. The tailor made optimization algorithm simultaneously considers alternative production paths and facility selection as well as product and resource specific parameters such as batch sizes, and setup and cleanup times. The collaborative planning concept allows all the plants to work simultaneously as partners in a supply chain resulting in higher transparency, greater flexibility, and reduced response time as a whole. The user interface supports monitoring production schedules graphically and provides custom-built utilities for manual changes to the production schedule, investigation of various what-if scenarios, and marketing queries. RID="*" ID="*" The authors would like to thank Hans-Otto Günther and Roland Heilmann for helpful comments on draft versions of this paper.     

Open-pit coal mine production sequencing incorporating grade blending and stockpiling options: An application from an Indian mine

Production scheduling is a crucial aspect of the mining industry. An optimal and efficient production schedule can increase the profits manifold and reduce the amount of waste to be handled. Production scheduling for coal mines is necessary to maintain consistency in the quality and quantity parameters of coal supplied to power plants. Irregularity in the quality parameters of the coal can lead to heavy losses in coal-fired power plants. Moreover, the stockpiling of coal poses environmental and fire problems owing to low incubation periods. This article proposes a production scheduling formulation for open-pit coal mines including stockpiling and blending opportunities, which play a major role in maintaining the quality and quantity of supplied coal. The proposed formulation was applied to a large open-pit coal mine in India. This contribution provides an efficient production scheduling formulation for coal mines after utilizing the stockpile coal within the incubation periods with the maximization of discounted cash flows. At the same time, consistency is maintained in the quality and quantity of coal to power plants through blending and stockpiling options to ensure smooth functioning.     

A hybrid genetic algorithm and tabu search for a multi-objective dynamic job shop scheduling problem

In most real manufacturing environments, schedules are usually inevitable with the presence of various unexpected disruptions. In this paper, a rescheduling method based on the hybrid genetic algorithm and tabu search is introduced to address the dynamic job shop scheduling problem with random job arrivals and machine breakdowns. Because the real-time events are difficult to express and take into account in the mathematical model, a simulator is proposed to tackle the complexity of the problem. A hybrid policy is selected to deal with the dynamic feature of the problem. Two objectives, which are the schedule efficiency and the schedule stability, are considered simultaneously to improve the robustness and the performance of the schedule system. Numerical experiments have been designed to test and evaluate the performance of the proposed method. This proposed method has been compared with some common dispatching rules and meta-heuristic algorithms that have been widely used in the literature. The experimental results illustrate that the proposed method is very effective in various shop-floor conditions.     

Applying the TOC thinking process in manufacturing: A case study

A case study is presented to illustrate the application of the Theory of Constraints thinking process logic tools in a manufacturing environment. The study firm performs design activities related to meeting future product requirements while concurrently meeting existing production schedules for the current design of the product. Current approaches to managing the firm's limited productive capacity do not allow for both design and production activities to occur simultaneously while meeting the customer's current product delivery schedule. Thus, despite their desire to satisfy their customer's future design requirements, management uses the majority of its production capacity to meet its customer's current product delivery schedule. This case study demonstrates how a team of employees used thinking process logic diagrams to document reality, identify a core conflict and problem, develop proposed changes to address the core problem, and create several detailed action plans to implement changes within the study organization. Initially, scenarios associated with some undesirable effects are used to understand how prevailing policies and behaviours result in less than desired production line performance. Then, a current reality tree is constructed to link the core problem or system constraint with the previously identified undesirable effects. Next, two major injections are developed to address the core problem in managing the production line as logically documented in a future reality tree. Finally, three transition trees are presented to guide the implementation of change at the study organization.     

Multicriteria scheduling using fuzzy theory and tabu search

Scheduling problems are frequently encountered in manufacturing systems. The results of job scheduling can have a great impact on the throughputs of systems. To maintain a high level of productivity of the systems, many objectives have to be taken into account simultaneously while evaluating the results of a scheduling arrangement. Unfortunately, in the high-dimension environment of considering these objectives, the performance evaluation over the criteria of interest becomes rather vague. Thus, it is very difficult to apply the traditional numerical methods to construct the models, though the measurement of these may not be fully compatible with the criteria of the scheduling problems. In this paper, the linguistic values of fuzzy set theory are introduced to evaluate each criterion and to represent its relative weight for the schedules of a multicriteria environment. The basic operations for the triangular fuzzy numbers and the calculations for obtaining the ranking of each aggregated linguistic evaluation for the quality of the schedule are explained clearly. According to the complex property of scheduling problems, a heuristic approach of tabu search is applied to solve the problems. The basic mechanism and spirits of the tabu search are also interpreted here. The overall procedure of the proposed approach is addressed by a numerical example. The aim is to propose an approach that can more easily apply the multicriteria technique.     

Flow shop scheduling with grid-integrated onsite wind power using stochastic MILP

Over the last decade, manufacturing companies have identified renewable energy as a promising means to cope with time-varying energy prices and to reduce energy-related greenhouse gas emissions. As a result of this development, global installed capacity of wind power has expanded significantly. To make efficient use of onsite wind power generation facilities in manufacturing, production scheduling tools need to consider the uncertainty attached to wind power generation along with changes in the energy procurement cost and in the products’ environmental footprints. To this end, we propose a solution procedure that first generates a large number of wind power scenarios that characterise the variability in wind power over time. Subsequently, a two-stage stochastic optimisation procedure computes a production schedule and energy supply decisions for a flow shop system. In the first stage, a bi-objective mixed integer linear programme simultaneously minimises the total weighted flow time and the expected energy cost, based on the generated wind power scenarios. In the second stage, energy supply decisions are adjusted based on real-time wind power data. A numerical example is used to illustrate the ability of the developed decision support tool to handle the uncertainty attached to wind power generation and its effectiveness in realising energy-related objectives in manufacturing.     

Dynamic scheduling of FMS using a real-time genetic algorithm

The paper presents a genetic algorithm capable of generating optimised production plans in flexible manufacturing systems. The ability of the system to generate alternative plans following part-flow changes and unforeseen situations is particularly stressed (dynamic scheduling). Two contrasting objectives represented by the reduction of machine idle-times, thanks to dynamic scheduling computation and the reduction of the makespan, are taken into account by the proposed system. The key-point is the real-time response obtained by an optimised evolutionary strategy capable of minimising the number of genetic operations needed to reach the optimal schedule in complex manufacturing systems.     

The infrastructure of the timed EOPNs-based multiple-objective real-time scheduling system for 300 mm wafer fab

Modern semiconductor wafer fabrication systems are changing from 200?mm to 300?mm wafer processing, and with the dual promises of more chips per wafer and economy of scale, leading semiconductor manufacturers are attracted to developing and implementing 300?mm wafer fabs. However, in today's dynamic and competitive global market, a successful semiconductor manufacturer has to excel in multiple performance indices, such as manufacturing cycle time and on-time delivery, and simultaneously optimize these objectives to reach the best-compromised system achievement. To cope with this challenge, in this paper, the infrastructure of a timed EOPNs-based multiple-objective real-time scheduling system (MRSS) is proposed to tackle complex 300?mm wafer fabs. Four specific performance objectives pursued by contemporary semiconductor manufacturers are integrated into a priority-ranking algorithm, which can serve as the initial scheduling guidance, and then all wafer lots will be dynamically dispatched by the real-time state-dependent dispatching system. This dispatching control system is timed EOPN-based and adopts a heterarchical organization that leads to a better real-time performance and adaptability. As the foundation of real-time schedule, the timed EOPNs modelling approach is expounded in detail, and the prototype of the MRSS simulation system is also provided.     

An empirical study of policies to integrate reactive scheduling and control in just-in-time job shop environments

In this paper, we compare the performance of policies for integrating reactive scheduling and control that differ in the way they interpret and dynamically reoptimize schedules in the face of contingencies. We conduct our analysis in the context of just-in-time job shop environments (?job shop problems with an objective of minimizing the sum of tardiness and inventory costs), subject to machine failures. We empirically evaluate the tradeoffs in schedule quality and computational time of different scheduling policies under different load conditions and different levels of uncertainty. Our results show that reactive procedures that selectively reoptimize a subset of the scheduling problems are capable of producing high-quality solutions in a fraction of the time required to generate brand new schedules.